Publikationer från Malmö universitet
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Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).ORCID-id: 0000-0001-9376-9844
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).ORCID-id: 0000-0002-8512-2976
2022 (engelsk)Inngår i: Information, E-ISSN 2078-2489, Vol. 13, nr 4, artikkel-id 203Artikkel, forskningsoversikt (Fagfellevurdert) Published
Abstract [en]

The use of artificial intelligence and machine learning techniques across all disciplines has exploded in the past few years, with the ever-growing size of data and the changing needs of higher education, such as digital education. Similarly, online educational information systems have a huge amount of data related to students in digital education. This educational data can be used with artificial intelligence and machine learning techniques to improve digital education. This study makes two main contributions. First, the study follows a repeatable and objective process of exploring the literature. Second, the study outlines and explains the literature's themes related to the use of AI-based algorithms in digital education. The study findings present six themes related to the use of machines in digital education. The synthesized evidence in this study suggests that machine learning and deep learning algorithms are used in several themes of digital learning. These themes include using intelligent tutors, dropout predictions, performance predictions, adaptive and predictive learning and learning styles, analytics and group-based learning, and automation. artificial neural network and support vector machine algorithms appear to be utilized among all the identified themes, followed by random forest, decision tree, naive Bayes, and logistic regression algorithms.

sted, utgiver, år, opplag, sider
MDPI , 2022. Vol. 13, nr 4, artikkel-id 203
Emneord [en]
AI, ML, DL, digital education, literature review, dropouts, intelligent tutors, performance prediction
HSV kategori
Identifikatorer
URN: urn:nbn:se:mau:diva-51752DOI: 10.3390/info13040203ISI: 000786209900001Scopus ID: 2-s2.0-85129306474OAI: oai:DiVA.org:mau-51752DiVA, id: diva2:1661970
Tilgjengelig fra: 2022-05-30 Laget: 2022-05-30 Sist oppdatert: 2024-02-05bibliografisk kontrollert

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Munir, HussanVogel, BahtijarJacobsson, Andreas

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